How does an image-processing application that classifies animals accelerate the development of a vehicle license plate number detector? In this talk, Dr. Yaqi Chen, Lead Data Scientist at Object Computing, demonstrates how ML practitioners can achieve a level of scalability and generalization that opens up a vast landscape of possibilities. Using a real-world example, she illustrates how a series of plug-and-play modules built across the data, model, and deployment stages dramatically simplify the process of building an end-to-end ML project. If you could benefit from an ML lifecycle framework that allows you to continue tackling complicated real-world challenges, while enjoying shorter development time, simplified bug isolation, and a cleaner code base, this talk is for you!
It could be argued that full testing of applications is just as important as the application itself. So any tool that makes testing easier and more natural is highly beneficial. Previously, there have been many Java News Briefs (here and here) that address testing functionality in isolation without requiring expensive resource setup/execution/teardown or when resources are not available. 2b1af7f3a8